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Each year, the second Thursday of November day is set aside to reflect on the way quality management can contribute to our work and our lives. Led by the Chartered Quality Institute (CQI) in the United Kingdom, World Quality Day provides a forum to reflect on how we implement more effective processes and systems that positively impact KPIs and business results — and celebrate outcomes and new insights.

This year’s theme is “Quality: A Question of Trust”.

We usually think of quality as an operations function. The quality system (whether we have quality management software implemented or not) helps us keep track of the health and effectiveness of our manufacturing, production, or service processes. Often, we do this to obtain ISO 9001:2015 certification, or achieve outcomes that are essential to how the public perceives us, like reducing scrap, rework, and customer complaints.

But the quality system encompasses all the ways we organize our business — ensuring that people, processes, software, and machines are aligned to meet strategic and operational goals. For example, QMS validation (which is a critical for quality management in the pharmaceutical industry), helps ensure that production equipment is continuously qualified to meet performance standards, and trust is not broken. Intelex partner Glemser Technologies explains in more detail in The Definitive Guide to Validating Your QMS in the Cloud. This extends to managing supplier relationships — building trust to cultivate rich partnerships in the business ecosystem out of agreements to work together.

This also extends to building and cultivating trust-based relationships with our colleagues, partners, and customers…

[This post is in response to ASQ’s February topic for the Influential Voices group, which asks: Where do you plan to take your career in 2016? What’s your view of careers in quality today—what challenges is this field facing? How can someone starting out in quality succeed?]

We are about to experience a paradigm shift in production, operations, and service: a shift that will have direct consequences on the principles and practice of design, development, and quality management. This “fourth industrial revolution” of cyber-physical systems will require more people in the workforce to understand quality principles associated with co-creation of value, and to develop novel business models. New technical skills will become critical for a greater segment of workers, including embedded software, artificial intelligence, data science, analytics, Big Data (and data quality), and even systems integration.

Over the past 20 years, we moved many aspects of our work and our lives online. And in the next 20 years, the boundaries between the physical world and the online world will blur — to a point where the distinction may become unnecessary.

Here is a vignette to illustrate the kinds of changes we can anticipate. Imagine the next generation FitBit, the personalized exercise assistant that keeps track of the number of steps you walk each day. As early as 2020, this device will not only automatically track your exercise patterns, but will also automatically integrate that information with your personal health records. Because diet strategies have recently been shown to be predominantly unfounded, and now researchers like Kevin Hall, Eran Elinav, and Eran Siegal know that the only truly effective diets are the ones that are customized to your body’s nutritional preferences [1], your FitBit and your health records will be able to talk to your food manager application to design the perfect diet for you (given your targets and objectives). Furthermore, to make it easy for you, your applications will also autonomously communicate with your refrigerator and pantry (to monitor how much food you have available), your local grocery store, and your calendar app so that food deliveries will show up when and only when you need to be restocked. You’re amazed that you’re spending less on food, less of it is going to waste, and you never have to wonder what you’re going to make for dinner. Your local grocery store is also greatly rewarded, not only for your loyalty, but because it can anticipate the demand from you and everyone else in your community – and create specials, promotions, and service strategies that are targeted to your needs (rather than just what the store guesses you need).

Although parts of this example may seem futuristic, the technologies are already in place. What is missing is our ability to link the technologies together using development processes that are effective and efficient – and in particular, coordinating and engaging the people who will help make it happen. This is a job for quality managers and others who study production and operations management

As the Internet of Things (IoT) and pervasive information become commonplace, the fundamental nature and character of how quality management principles are applied in practice will be forced to change. As Eric Schmidt, former Chairman of Google, explains: “the new age of artificial intelligence is beginning, and it’s a big deal.” [2] Here are some ways that this shift will impact researchers and practitioners interested in quality:

Strategic deployment of IoT technologies will help us simultaneously improve our use of enterprise assets, reduce waste, promote sustainability, and coordinate people and machines to more effectively meet strategic goals and operational targets.

Smart materials, embedded in our production and service ecosystems, will change our views of objects from inert and passive to embedded and engaged. For example, MIT has developed a “smart band-aid” that communicates with a wound, provides visual indicators of the healing process, and delivers medication as needed. [3] Software developers will need to know how to make this communication seamless and reliable in a variety of operations contexts.

Our technologies will be able to proactively anticipate the Voice of the Customer, enabling us to meet not only their stated and implied needs, but also their emergent needs and hard-to-express desires. Similarly, will the nature of customer satisfaction change as IoT becomes more pervasive?

Cloud and IoT-driven Analytics will make more information available for powerful decision-making (e.g. real-time weather analytics), but comes with its own set of challenges: how to find the data, how to assess data quality, and how to select and store data with likely future value to decision makers. This will be particularly challenging since analytics has not been a historical focus among quality managers. [4]

Smart manufacturing systems will implement real time communication between people, machines, materials, factories and warehouses, supply chain partners, and logistics partners using cloud computing. Production systems will adapt to demand as well as environmental factors, like the availability of resources and components. Sustainability will be a required core capability of all organizations that produce goods.

Cognitive manufacturing will implement manufacturing and service systems capable of perception, judgment, and improving quality autonomously – without the delays associated with human decision-making or the detection of issues.

Cybersecurity will be recognized as a critical component of all of the above. For most (if not all) of these next generation products and production systems, quality will not be possible without addressing information security.

The nature of quality assurance will also change, since products will continue to learn (and not necessarily meet their own quality requirements) after purchase or acquisition, until the consumer has used them for a while. In a December 2015 article I wrote for Software Quality Professional, I ask “How long is the learning process for this technology, and have [product engineers] designed test cases to accommodate that process after the product has been released? The testing process cannot find closure until the end of the ‘burn-in’ period when systems have fully learned about their surroundings.” [5]

We will need new theories for software quality practice in an era where embedded artificial intelligence and technological panpsychism (autonomous objects with awareness, perception, and judgment) are the norm.

How do we design quality into a broad, adaptive, dynamically evolving ecosystem of people, materials, objects, and processes? This is the extraordinarily complex and multifaceted question that we, as a community of academics and practitioners, must together address.

Just starting out in quality? My advice is to get a technical degree (science, math, or engineering) which will provide you with a solid foundation for understanding the new modes of production that are on the horizon. Industrial engineering, operations research, industrial design, and mechanical engineering are great fits for someone who wants a career in quality, as are statistics, data science, manufacturing engineering, and telecommunications. Cybersecurity and intelligence will become increasingly more central to quality management, so these are also good directions to take. Or, consider applying for an interdisciplinary program like JMU’s Integrated Science and Technology where I teach. We’re developing a new 21-credit sector right now where you can study EVERYTHING in the list above! Also, certifications are a plus, but in addition to completing training programs be sure to get formally certified by a professional organization to make sure that your credentials are widely recognized (e.g. through ASQ and ATMAE).

June 24, 1980 is kind of like July 4, 1776 for quality management… that’s the pivotal day that NBC News aired its one hour and 16 minute documentary called “If Japan Can, Why Can’t We?” introducing W. Edwards Deming and his methods to the American public. The video has been unavailable for years, butas of just last week, it’s been posted on YouTube. So my sophomore undergrads in Production & Operations Management took a step back in time to get a taste of the environment in the manufacturing industry in the late 1970’s, and watched it during class this week.

The last time I watched it was in 1997, in a graduate industrial engineering class. It didn’t feel quite as dated as it does now, nor did I have the extensive experience in industry as a lens to view the interviews through. But what did surprise me is that the core of the challenges they were facing aren’t that much different than the ones we face today — and the groundbreaking good advice from Deming is still good advice today.

Before 1980, it was common practice to produce a whole bunch of stuff and then check and see which ones were bad, and throw them out. The video provides a clear and consistent story around the need to design quality in to products and processes, which then reduces (or eliminates) the need to inspect bad quality out.

It was also common to tamper with a process that was just exhibiting random variation. As one of the line workers in the documentary said, “We didn’t know. If we felt like there might be a problem with the process, we would just go fix it.” Deming’s applications of Shewhart’s methods made it clear that there is no need to tamper with a process that’s exhibiting only random variation.

Both workers and managers seemed frustrated with the sheer volume of regulations they had to address, and noted that it served to increase costs, decrease the rate of innovation, and disproportionately hurt small businesses. They noted that there was a great need for government and industry to partner to resolve these issues, and that Japan was a model for making these interactions successful.

Narrator Lloyd Dobyns remarked that “the Japanese operate by consensus… we, by competition.” He made the point that one reason Japanese industrial reforms were so powerful and positive was that their culture naturally supported working together towards shared goals. He cautioned managers that they couldn’t just drop in statistical quality control and expect a rosy outcome: improving quality is a cultural commitment, and the methods are not as useful in the absence of buy-in and engagement.

The video also sheds light on ASQ’s November question to the Influential Voices, which is:“What’s the key to talking quality with the C-Suite?” Typical responses include: think at the strategic level; create compelling arguments using the language of money; learn the art of storytelling and connect your case with what it important to the executives.

But I think the answer is much more subtle. In the 1980 video, workers comment on how amazed their managers were when Deming proclaimed that management was responsible for improving productivity. How could that be??!? Many managers at that time were convinced that if a productivity problem existed, it was because the workers didn’t work fast enough, or with enough skill — or maybe they had attitude problems! Certainly not because the managers were not managing well. Implementing simple techniques like improving training programs and establishing quality circles (which demonstrated values like increased transparency, considering all ideas, putting executives on the factory floor so they could learn and appreciate the work being done, increasing worker participation and engagement, encouraging work/life balance, and treating workers with respect and integrity) were already demonstrating benefits in some U.S. companies. But surprisingly, these simple techniques were not widespread, and not common sense.

Just like Deming advocated, quality belongs to everyone. You can’t go to a CEO and suggest that there are quality issues that he or she does not care about. More likely, the CEO believes that he or she is paying a lot of attention to quality. They won’t like it if you accuse them of not caring, or not having the technical background to improve quality. The C-Suite is in a powerful position where they can, through policies and governance, influence not only the actions and operating procedures of the system, but also its values and core competencies — through business model selection and implementation.

What you can do, as a quality professional, is acknowledge and affirm their commitment to quality. Communicate quickly, clearly, and concisely when you do. Executives have to find the quickest ways to decompose and understand complex problems in rapidly changing external environments, and then make decisions that affect thousands (and sometimes, millions!) of people. Find examples and stories from other organizations who have created huge ripples of impact using quality tools and technologies, and relate them concretely to your company.

Let the C-Suite know that you can help them leverage their organization’s talent to achieve their goals, then continually build their trust.

Last weekend, I had the opportunity to talk to James Siegal, the President of KaBOOM! – a non-profit whose mission is lighthearted, but certainly not frivolous: to bring balanced and active play into the daily lives of all kids! James is another newBusiness Innovation Factory (BIF) storyteller for 2015… and I wanted to find out how I could learn from his experiences to bring a sense of play into the work environment. (For me, that’s at a university, interacting with students on a daily basis.)

Over the past 20 years, KaBOOM! has built thousands of playgrounds, focusing on children growing up in poverty. By enlisting the help of over a million volunteers, James and his organization have mobilized communities using a model that starts with kids designing their dream playgrounds. It’s a form of crowdsourced placemaking.

Now, KaBOOM! is thinking about a vision that’s a little broader: driving social change at the city level. Doing this, they’ve found, requires answering one key question: How can you integrate play into the daily routine for kids and families? If play is a destination, there are “hassle factors” that must be overcome: safety, travel time, good lighting, and restroom facilities, for starters. So, in addition to building playgrounds, KaBOOM! is challenging cities to think about integrating play everywhere — on the sidewalk, at the bus stop, and beyond.

How can this same logic apply to organizations integrating play into their cultures? Although KaBOOM! focuses on kids, he had some more generalizable advice:

The desire for play has to be authentic, not forced. “We truly value kids, and we truly value families. Our policies and our culture strive to reflect that.” What does your organization value at its core? Seek to amplify the enjoyment of that.

“We take our work really seriously,” he said. “We don’t take ourselves too seriously. You have to leave your ego at the door.” Can your organization engage in more playful collaboration?

We drive creativity out of kids as they grow older, he noted. “Kids expect to play everywhere,” and so even ordinary elements like sidewalks can turn into experiences. (This reminded me of how people decorate the Porta-Potties at Burning Man with lights and music… although I wouldn’t necessarily do the same thing to the restrooms at my university, it did make me think about how we might make ordinary places or situations more fun for our students.)

KaBOOM! is such a unique organization that I had to ask James: what’s the most amazing thing you’ve ever observed in your role as President? He says it’s something that hasn’t just happened once… but happens every time KaBOOM! organizes a new playground build. When people from diverse backgrounds come together with a strong shared mission, vision, and purpose, you foster intense community engagement that yields powerful, tangible results — and this is something that so many organizations strive to achieve.

This post was motivated by two recent tweets by Dr. Diego Kuonen, Principal of Statoo Consulting in Switzerland (who you should definitely follow if you don’t already – he’s one of the only other people in the world who thinks about data science and quality). First, he shared a slide show fromCIO Insightwith this clickbaity title, bound to capture the attention of any manager who cares about their bottom line (yeah, they’re unicorns):

I’m so happy this message is starting to enter corporate consciousness, because I lived it throughout the decade of the 2000’s — working on data management for the National Radio Astronomy Observatory (NRAO). I published several papers during that time that present the following position on this theme (links to the full text articles are at the bottom of this post):

First, storing data means you’ve saved it to physical media; archiving data implies that you are storing data over a longer (and possibly very long) time horizon.

Even though storage is cheap, don’t store (or archive) everything. Inventories have holding costs, and data warehouses are no different (even though those electrons are so, so tiny).

Archiving data that is of dubious quality is never advised. (It’s like piling your garage full of all those early drafts of every paper you’ve ever written… and having done this, I strongly recommend against it.)

Sometimes it can be hard to tell whether the raw data we’re collecting is fundamentally good or bad — but we have to try.

Data science provides fantastic techniques for learning what is meant by data quality, and then automating the classification process.

The intent of whoever collects the data is bound to be different than whoever uses the data in the future.

If we do not capture intent, we are significantly suppressing the potential that the data asset will have in the future.

Although I hadn’t seen this when I was deeply enmeshed in the problem long ago, it totally warmed my heart when Diego followed up with this quote from Deming in 1942:

In my opinion, the need for a dedicated focus onunderstanding what we mean by data quality (for our particular contexts) and then working to make sure we don’t load up our Big Data opportunities with Bad Data liabilities will be the difference between competitive and combustiblein the future. Mind your data quality before your data science. It will also positively impact the sustainability of your data archive.

Papers where I talked about why NOT to archive all your data are here:

I believe that the data scientist “unicorn” is hidden right in front of our faces; the purpose of this post is to help you find it.First, we’ll take a look at some models, and then I’ll present my version of what a data scientist is (and how this person can become “great”).

#1 Drew Conway’s popular “Data Science Venn Diagram” — created in 2010 — characterizes the data scientist as a person with some combination of skills and expertise in three categories (and preferably, depth in all of them): 1) Hacking, 2) Math and Statistics, and 3) Substantive Expertise (also called “domain knowledge”).

Later, he added that there was a critical missing element in the diagram: that effective storytelling with data is fundamental. The real value-add, he says, is being able to construct actionable knowledge that facilitates effective decision making. How to get the “actionable” part? Be able to communicate well with the people who have the responsibility and authority to act.

“To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods. On the flip-side, substantive expertise plus math and statistics knowledge is where most traditional researcher falls. Doctoral level researchers spend most of their time acquiring expertise in these areas, but very little time learning about technology. Part of this is the culture of academia, which does not reward researchers for understanding technology. That said, I have met many young academics and graduate students that are eager to bucking that tradition.” — Drew Conway, March 26, 2013

#2 In 2013, Harlan Harris (along with his two colleagues, Sean Patrick Murphy and Marck Vaisman) published a fantastic study where they surveyed approximately 250 professionals who self-identified with the “data science” label. Each person was asked to rank their proficiency in each of 22 skills (for example, Back-End Programming, Machine Learning, and Unstructured Data). Using clustering, they identified four distinct “personality types” among data scientists:

Data Businesspeople who are most focused on the information itself and how it is applied to business decisions. (These people were least likely to identify with the “data scientist” label.)

Data Researchers, those deeply familiar with the mathematical and statistical underpinnings of the work, who can develop new techniques as necessary (in addition to correctly selecting from available techniques).

As a manager, you might try to cut corners by hiring all Data Creatives(*). But then, you won’t benefit from the ultra-awareness that theorists provide. They can help you avoid choosing techniques that are inappropriate, if (say) your data violates the assumptions of the methods. This is a big deal! You can generate completely bogus conclusions by using the wrong tool for the job. You would not benefit from the stress relief that the Data Developers will provide to the rest of the data science team. You would not benefit from the deep domain knowledge that the Data Businessperson can provide… that critical tacit and explicit knowledge that can save you from making a potentially disastrous decision.

“The data scientist’s skills – advanced analytics, data integration, software development, creativity, good communications skills and business acumen – often already exist in an organisation. Just not in a single person… likely to be spread over different roles, such as statisticians, bio-chemists, programmers, computer scientists and business analysts. And they’re easier to find and hire than data scientists.”

They cite British Airways as an exemplar:

“[British Airways] believes that data scientists are more effective and bring more value to the business when they work within teams. Innovation has usually been found to occur within team environments where there are multiple skills, rather than because someone working in isolation has a brilliant idea, as often portrayed in TV dramas.”

Their position is you can’t get all those skills in one person, so don’t look for it. Just yesterday I realized that if I learn one new amazing thing in R every single day of my life, by the time I die, I will probably be an expert in about 2% of the package (assuming it’s still around).

#4 Others have chimed in on this question and provided outlines of skill sets, such as:

IBM: “part analyst, part artist” skilled in “computer science and applications, modeling, statistics, analytics and math… [and] strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.”

SAS: “a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved. They’re part mathematician, part computer scientist and part trend-spotter.” (Doesn’t that sound exciting?)

DataJobs.Com: well, these guys just took Drew Conway’s Venn diagram and relabeled it.

#5 My Answer to “What is a Data Scientist?”:A data scientist is a sociotechnical boundary spanner who helps convert data and information into actionable knowledge.

Based on all of the perspectives above, I’d like to add that the data scientist must have an awareness of the context of the problems being solved: social, cultural, economic, political, and technological. Who are the stakeholders? What’s important to them? How are they likely to respond to the actions we take in response to the new knowledge data science brings our way? What’s best for everyone involved so that we can achieve sustainability and the effective use of our resources? And what’s with the word “helps” in the definition above? This is intended to reflect that in my opinion, a single person can’t address the needs of a complex data science challenge. We need each other to be “great” at it.

A data scientist is someone who can effectively span the boundaries between

1) understanding social+ context,

2) correctly selecting and applying techniques from math and statistics,

3) leveraging hacking skills wherever necessary,

4) applying domain knowledge, and

5) creating compelling and actionable stories and connections that help decision-makers achieve their goals. This person has a depth of knowledge and technical expertise in at least one of these five areas, and a high level of familiarity with each of the other areas (commensurate with Harris’ T-model). They are able to work productively within a small team whose deep skills span all five areas.

It’s data-driven decision making embedded in a rich social, cultural, economic, political, and technological context… where the challenges may be complex, and the stakes (and ultimately, the benefits) may be high.

(*) Disclosure: I am a Data Creative!

(**)Quality professionals (like Six Sigma Black Belts) have been doing this for decades. How can we enhance, expand, and leverage our skills to address the growing need for data scientists?

So much innovation in STEM is fueled by imagination and exploration, and in my opinion, we don’t communicate that very well to younger people. A great gateway drug for this purpose is art. There’s even a movement underway to expand out vision of STEM, and more tightly and more essentially integrate aesthetics, form, design, and fun into what we do via STEAM (Science, Technology, Engineering, Art, and Math).

STEAM doesn’t advocate just doing the arts alongside more traditional science and engineering. It actually requires that we look towards how we can use STEAM to create meaning for ourselves and our communities. In other words, it can help us get our mind off of science and engineering to understand and control the world around us – and focus more on how beautiful and intriguing things are that we can learn in those domains.

The picture above is the interactive zonohedral dome (or “zome”) that our students created specifically to engage others in the fun of integrated science and engineering. Here’s how they summarize their project:

As our communities expand rapidly, both physically and digitally, we can lose our sense of connection and togetherness. Interactive and participatory art interventions cultivate community by provoking engagement in unexpected areas. In this project, the prototype for an interactive zonohedral dome (or “zome”) was constructed as a proof of concept for an art intervention to engage students in collaborative STEM (Science, Technology, Engineering, and Math ) learning, by creating feelings of connection with the technology and with each other. Consequently, it demonstrates the values of the STEAM (Science, Technology, Engineering, Art, and Math) movement in education. Design elements (and an assessment approach) were selected based on a comprehensive literature review which focused on the aspects of engagement that would boost participants’ interest in and proficiency with STEM subjects.

A zome is a structure that supports itself solely due to its geometry. No nails or glue are used in the construction. The interactive nature of the structure emerges from sensors that detect occupancy, with music and lights automatically responding to the pattern of people entering and leaving the zome. Many technologies were combined to create this experience, including SketchUp (to design the components), Makerbot Replicator II (to build the structure), Arduino (to detect occupancy via phototransistors), LightShowPi (to generate Fast Fourier transforms of music files and control the frequency and amplitude of audio communicated via LEDs), and RaspberryPi (a microcomputer to run LightShowPi and translate the signals from the Arduino to play audio at pre-designated decibel levels).

We’ll post a video of the zome in action very soon. It’s so fun to look at, and play with… and what better way to learn programming than to make a structure respond to the presence and motion of the people around it?

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